For years, data catalogers have been essential to ensuring product data is clean, enriched, and accurate. Their expertise in identifying missing attributes, standardizing inconsistent data, and verifying classifications has been instrumental in helping organisations manage procurement, inventory, and supply chain operations efficiently.
In industries like Maintenance, Repair, and Operations (MRO), where organizations handle vast product catalogs containing 100,000 to over 1,000,000 items, manual data maintenance has been the foundation of data accuracy. However, as product datasets grow, catalogers increasingly face challenges that demand AI-assisted solutions to streamline their work and enhance productivity.
The Manual Data Cleansing & Enrichment Process
- Searching multiple sources to find accurate product descriptions, specifications, and attributes.
- Identifying and correcting errors such as duplicate entries, spelling mistakes, and inconsistent formatting.
- Manually categorising products into industry classification systems like UNSPSC.
- Verifying data accuracy to ensure compliance with procurement, inventory, and regulatory standards.
While this manual process remains critical for quality assurance, the sheer volume of data now being handled requires AI-driven support to allow catalogers to focus on higher-value tasks rather than spending excessive time on repetitive data corrections.
The Challenges of Manual Data Management
With ever-expanding product catalogs, relying solely on manual data maintenance is becoming increasingly difficult. MRO organisations must maintain data consistency and accuracy at scale, which can be challenging when dealing with millions of data points.
Key Challenges in Manual Product Data Management
- Time-Intensive: Cleaning and enriching each item manually can take 5 to 15 minutes, making it difficult to manage large datasets efficiently.
- Inconsistent Data Quality: Even the most experienced catalogers may encounter errors in classification, attribute entry, and formatting when handling high volumes of data.
- Duplicate & Missing Data: Poorly structured data results in duplicate purchases, overstocking, and procurement inefficiencies.
- Operational Disruptions: Incorrect or missing product data can cause:
- Procurement delays
- Stock shortages
- Equipment downtime due to incorrect spare parts
- Inaccurate spend analysis and reporting
- Compliance & Regulatory Issues: Poor data quality makes it harder to ensure audit readiness and regulatory compliance, particularly for industries with stringent requirements.
To meet these growing challenges, companies are enhancing their data cataloging efforts with AI-driven solutions—not to replace catalogers, but to support them with faster and more accurate processing.
The Rise of AI in Product Data Management
AI is not replacing data catalogers—it’s enhancing their capabilities by automating repetitive tasks and improving data accuracy. AI-powered tools help identify errors, standardise formatting, and enrich missing attributes, allowing catalogers to focus on quality assurance and complex data validation.
How AI is Improving Product Data Management
- Automated Error Detection & Correction: AI scans large datasets and identifies inconsistencies, duplicates, and missing information faster than manual methods.
- Data Standardisation: AI ensures product descriptions, attributes, and classifications are formatted consistently across ERP, EAM, PIM, and MDM systems.
- Faster Classification: AI automates UNSPSC categorisation, eliminating the need for manual sorting and tagging while still allowing catalogers to review and validate final outputs.
- Enhanced Attribute Enrichment: AI fills in missing data, such as supplier details, technical specifications, and compliance certifications, reducing manual lookup time.
With AI handling bulk data structuring and classification, data catalogers remain integral to final validation.
The Limitations of General AI in Product Data Management
While AI has transformed many industries, not all AI solutions are built for product data management. General AI (GenAI) models, such as those used in chatbots and content generation, struggle with accuracy when handling industrial data.
Why General AI Falls Short in Product Data Management
- Lower Accuracy: GenAI lacks industry-specific training, leading to incorrect product classifications and unreliable enrichment.
- Hallucinations & Fabricated Outputs: GenAI often generates incorrect or misleading product attributes, making it unusable for procurement and inventory management.
- Lack of Context Awareness: Generic AI models cannot interpret industry standards like UNSPSC, ISO, or regulatory compliance requirements, resulting in inconsistent categorisation.
For AI to be truly effective in product data management, it must be trained on high-quality MRO and industrial data and work alongside catalogers to refine and validate outputs. This is where AICA stands apart.
How AICA Solves the AI Accuracy Problem
AICA has developed an industry-leading AI-driven solution that delivers highly accurate, automated product data cleansing, enrichment, and classification—while keeping manual QA/QC processes intact to ensure high-quality data governance.
How AICA’s AI Solution Works
- Trained on High-Quality MRO Data – Unlike generic AI models, AICA’s algorithms are built specifically for industrial product data, ensuring higher accuracy and reliability.
- Over 90% Accuracy – AICA’s AI-driven classification and enrichment deliver unmatched precision, eliminating common GenAI errors while still allowing for human validation where necessary.
- Seamless ERP, EAM, MDM, and PIM Integration – Our Agentic AI integrates directly into enterprise systems via API, making it easy for organisations to adopt without disrupting existing workflows.
- Automated Error Detection & Correction – As new data enters a system, AICA automatically detects inconsistencies, missing attributes, and classification errors—correcting them in real time while allowing catalogers to review critical cases.
By using AI to enhance data cataloging efforts, organisations can increase efficiency, reduce errors, and improve data governance, while ensuring that manual QA/QC processes remain central to data validation.
Conclusion
AI-powered product data management is not about replacing human expertise—it’s about augmenting it. Data catalogers remain essential, particularly for poor-quality data that requires human validation. However, AI can automate repetitive tasks, improve efficiency, and ensure data accuracy at scale.
Looking to enhance your product data management strategy? Contact AICA today to learn how our AI-powered solutions can streamline your data cataloging efforts while keeping manual QA/QC at the forefront of data accuracy.
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